Here's a small backpropagation neural network that counts and an example and an explanation for how it.Missing. Program Gallery; Proof Gallery; About. The algorithm to do so is called backpropagation. 20 thoughts on “ Neural Networks and the Backpropagation. Cambiare Software Autoradio Chinese. Amazon S3 File Upload Api Company.

I am focusing on program of backpropagation. I have trouble on writing a program that if, i have 7(more than 1) inputs and 4 outputs. What should i write? Is it same as the 1 input to 1 output program?

I have tried one.but not successful. For example in the loading data; X1 = [0,1,2,3,4,5];%input X2 = [0,1,2,3,4,5];%input X3 = X4 = X5 = X6 = Y1 = [3,6,9,12,15]; Y2 = Y3 = Y4 = train_inp = [X1',X2'];%setting input train_out = [Y'];%seting Out put% check same number of patterns in each if size(train_inp,1) ~= size(train_out,1) disp('ERROR: data mismatch') return end or, in the other parts, is it any exceptional 'additional'?

A novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling some of the parameters, such as speed, torque, flux, voltage, current, etc. Of the induction motor is presented in this paper.

Induction motors are characterized by highly non-linear, complex and time-varying dynamics and inaccessibility of some of the states and outputs for measurements. Hence it can be considered as a challenging engineering problem in the industrial sector. Various advanced control techniques has been devised by various researchers across the world. Some of them are based on the fuzzy techniques.

Fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base, which is written on the previous experiences & these rules are fired which is random in nature. As a result of which, the outcome of the controller is also random & optimal results may not be obtained. Selection of the proper rule base depending upon the situation can be achieved by the use of an ANFIS controller, which becomes an integrated method of approach for the control purposes & yields excellent results, which is the highlight of this paper. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm.

This integrated approach improves the system performance, cost-effectiveness, efficiency, dynamism, reliability of the designed controller. The simulation results presented in this paper show the effectiveness of the method developed & has got faster response time or settling times. Further, the method developed has got a wide number of advantages in the industrial sector & can be converted into a real time application using some interfacing cards.